Evidence map›Paper›PMID 32798139›Full record

ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2021

Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms.

Abigail Dickinson, Manjari Daniel, Andrew Marin, Bilwaj Gaonkar, Mirella Dapretto, Nicole M McDonald, Shafali Jeste

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 3 pooled it
5.0field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 62 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Abigail DickinsonSemel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, California. Electronic address: adickinson@mednet.ucla.edu.
Manjari DanielSemel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, California.
Andrew MarinDepartment of Psychology, University of California, San Diego, San Diego, California.
Bilwaj GaonkarDepartment of Neurosurgery, Ronald Reagan UCLA Medical Center, University of California, Los Angeles, California.
Mirella DaprettoAhmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, California.
Nicole M McDonaldSemel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, California.
Shafali JesteSemel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, California.
University of California, Los Angeles · USUniversity of California San Diego · US

Funding

Understanding Repetitive Behavior in AutismP50HD055784 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BOOKHEIMER, SUSAN Y · 2007 to 2021
$32.0M
UCLA IDDRC: Translational CoreP50HD103557 · NICHD · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Peyman Golshani, SUMA JACOB · 2020 to 2026
$9.6M
NICHD NIH HHS P50 HD055784NICHD NIH HHS P50 HD103557
6 · The paper itself

Abstract

backgroundFunctional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, we asked whether electroencephalography measures of neural connectivity at 3 months of age predict autism symptoms at 18 months.

methodsSpontaneous electroencephalography data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12 Hz). Support vector regression analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Toddler Module of the Autism Diagnostic Observation Schedule, Second Edition.

resultsAutism Diagnostic Observation Schedule scores predicted by support vector regression algorithms trained on 3-month electroencephalography data correlated highly with Autism Diagnostic Observation Schedule scores measured at 18 months (r = .76, p = .02, root-mean-square error = 2.38). Specifically, lower frontal connectivity and higher right temporoparietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The support vector regression model did not predict cognitive abilities at 18 months (r = .15, p = .36), suggesting specificity of these brain patterns to ASD.

conclusionsUsing a data-driven, unbiased analytic approach, neural connectivity across frontal and temporoparietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.

Indexed as

Autism Spectrum DisorderAutistic DisorderAdultBiomarkersBrainElectroencephalographyHumansInfantBiomarkersAutism spectrum disorderElectroencephalographyFunctional connectivityInfancyMachine learningPrediction

Identifiers

PMID32798139
PMCPMC7736067
OpenAlexW3034721220

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.